Good afternoon, and welcome to the last session of Software today. My name is Blair Abernethy. I'm the software analyst, one of the software analysts here at Rosenblatt. Please help me welcome Pegasystems. We have Ken Stillwell, Ken's CFO of the company and COO, as well as Don Schuerman, CTO of the business, and Peter, Peter Welburn, who is the IR manager. We've got lots of, lots of firepower here with us at Pega today. I appreciate all of you guys spending the time. This, you know, you guys have participated in this event before. You know, our focus is around product and particularly around leveraging AI in your business. We're gonna start with a few questions, I think, for for Don, and maybe maybe start with Don, just giving us a a little bit of a an overview of Pega from your position and and sort of what you what your role is at the business. Yeah. So, so I'm, I'm Pega's CTO. I often describe that as Chief Translation Officer. I think a lot of what we do in a technology business is make sure that we can translate what is oftentimes pretty sophisticated technology into real value that our clients can get. That translation works both ways. Of course, we've got to make sure we're understanding our clients' needs and translating that back into our roadmap and our software strategies. I think that's been especially true over the past, you know, eight to nine months as everyone and their CEO has become fixated on generative AI, and what it means for the business, and how it's going to transform. It's been, it's been interesting for us to step into this space. Pega is a low-code platform for AI-powered decisioning and workflow automation. We've got decades of experience of using analytical AI models, business rules, automation capabilities like robotics and process orchestration, and case management to drive mission-critical, customer-facing processes for our clients at, at pretty significant scale. As a business, we get used across large clients in financial services, in insurance, in healthcare, in telecommunications, in manufacturing, at the, you know, federal and large state level, in five big areas of their business, in terms of how they engage their customers and really personalize that engagement to have very one-to-one relationships with the customer. How they drive onboarding processes and acquire new customers, new partners. Think of things like KYC processes and banking, where there's a sort of regulated process that needs to get followed to onboard a new customer. Customer servicing, you know, large portions of, or, or pretty much every customer service interaction in these organizations ends up in being a workflow that needs to be automated. We drive a lot of the automation of those customer service workflows, and, and many customers have evolved that to being their customer service desktop and platform. Core operations, so things like banking operations, healthcare and insurance claims, and then, what I call resolving exceptions, fixing things when they go wrong. This could be payment exceptions, financial crimes investigations, etc. You know, any, a decisioning and workflow platform used across the customer life cycle by our clients. Great! Don, you've, you know, you've been with Pega for 20+ years here. You're not new to AI at Pega. Maybe you can just talk about how things have evolved there and sort of what you're really working on this year that's adding value for customers. Yeah. just to go back a little bit, you know, a lot of Pega's founding was based in the concept of expert systems, which were really rules-based systems. Yeah ... some of the early generations of AI, and using that to automate decisions in complicated business processes like, you know, payment investigations and payment disputes. We, going back now about 10 years- 15 years, we acquired capability in what I would call the analytical AI space. Being able to use predictive models, machine learning models, to predict, and then use those predictions to drive Next-Best-Action or the right conversations with customers in a highly personalized way, and that's grown to be a significant part of our business. We, we have clients like Commonwealth Bank of Australia. You know, people, people like to talk about this idea of private AI models. Well, Commonwealth Bank of Australia is running many thousand private AI models that they've built using their own data, but Pega's decisioning engine, and those are machine learning models that are constantly getting feedback and improving the efficacy at which Commonwealth Bank of Australia and clients like them are able to engage with their customers and have the right conversation, whether that's a retention conversation, or a cross-sell, upsell, share of wallet conversation, or maybe just a servicing conversation, or even things like collections. We've taken that expertise and expanded it into other areas. As you, as I said, we do a lot of workflow automation. Well, workflow automation means we have a lot of structured data about how work has historically been done. We've been able to use that predictive and analytical model to deploy what we call process AI. That we can predict, for example, when a workflow is gonna miss an SLA and automatically drive an escalation based on that. Again, these are predictive analytical AI models that we're running at scale. In recent years, we've added in things like natural language processing, so we can automatically take in emails, convert that from unstructured into structured text. We've added voice AI, so that we can take and automatically transcript, say, transcribe a customer service call. Then, over the last, you know, nine to twelve months, we've been looking at ways to integrate generative AI. The next generation of these large language models that are focused less on analytical decision-making, and more on actually generation of text and images. Summarizing text, or generating new text, or generating new image. As we've been doing that work in our upcoming release, which we it's gonna be called Pega Infinity '23, and it'll be GA in the coming weeks. We've got about 20-25 new capabilities embedded in that release that explicitly use generative AI models to improve low-code developer productivity, the ability of marketers to personalize interactions via one-to-one, the ability of customer service agents to better engage and support their customers, the ability of business leaders to get visibility into how work and processes are getting done inside their organizations. We're really excited about using generative AI to complement the existing analytical AI and automation capabilities that we have in the platform. That's great, Don. You know, when you look at what you've done in the past, leveraging AI, and then now what you're looking at with this new release, with GenAI and all the sort of features there, How are you monetizing that? How does, you know, if a customer has the Pega Infinity platform, are they getting all of this? Do they pay extra for some of this? 'Cause things like you know, generative AI, it comes at a cost, an operational cost from your perspective as well, right? Yeah. Let me, let me separate some of the analytical AI from the generative AI stuff, just because I think, I think they're, they're important to understand both. We've historically had models for monetizing the analytical AI. We generally do that, you know, ultimately based on the number of decisions that somebody, that an organization is making. You make more decisions, you're theoretically getting more value from what the AI is bringing you, and, and, and then therefore, therefore, we monetize based on that. And that's true for things like next-best-action. That's true for things like Process AI, which I discussed. There's also been things that we've built, like Voice AI, which is an add-on license. For our clients who have customer service already, Voice AI and the ability to automatically transcribe a conversation is an add-on to that existing. As we look into the generative AI space, there, we talked on a recent, I think our CEO talked on a recent earnings call, sort of three ways that we see that being monetized. I think the first two are more near term, which is, one, we're gonna drive additional volume of cases into the Pega platform. The way that we drive most of our contracts on Pega is what we call case-based, and you can think of that as, like, a usage-based pricing model. A case is like a workflow, so a customer service request would be a case, or a claim that you process, or a new loan application. That's a case. Drive more cases on Pega, you're driving more value and automation into your business. We're driving additional revenue or ACV based on that. Because the generative AI stuff, a lot of it will allow our clients to deploy more cases faster and easier than they ever have before, we anticipate that driving more case volumes for us. Yeah. We're Well- The generative... Oh, Ken, Ken, go ahead. I was just gonna add just, just one, add-on to what Don just said, Blair, which is, I think, so, as clients use Pega more and more, naturally, as the case volume goes up, the economics get better in terms of the relationship between Pega and the client. The client also, the more cases that the, that the clients are pushing through Pega, the less likely humans on their teams need to interact. The benefit is not just more volume, it's that their cost to handle goes down materially- Yeah ... because they're driving through an intelligent automation platform using AI. The actual human interaction, the percentage of cases that need touched by a human, hopefully goes down dramatically, right? That's the value proposition. It's, it's really the customer spend goes down materially, our spend with our, our, our participation in that spend goes up proportionate to the value we're providing. It's a win-win for us and our clients. Yeah. you're shifting, labor, if you will, onto your... Automating more labor onto your platform, so they, they pay a little more to Pega, but their cost, their needs, you know, other capacity needs drop, right? The speed of the transaction goes up. Goes up. That you're getting away from a client, a person waiting on the phone for 20 minutes and dealing with a human that may have... You're able to, you're able to clear a lot of these actions using AI, either by communicating with the client or, quite frankly, communicating with nobody, and just stay straight through processing as much as you can. Yeah, that's the value proposition. We're also, the Generative AI features that we're putting in Infinity '23, are only available on Pega Cloud. Our SaaS managed service offering. Yeah. We're seeing that drive a lot of interest in our clients about moving some of their existing applications, migrating them onto Pega Cloud. Which both drives ultimately lower TCO and faster response, better for them, but that also drives additional cloud subscription and additional revenue for us. Then longer, longer term, we also anticipate there being generative AI features that are add-ons to our existing licenses. So there will be features that in order to take advantage of them, clients may need to pay additional, additional fees as well. Remind us where your customer base is today. Vis-a-vis, you know, the client-managed cloud versus the Pega Cloud, just to talk about. Our, our, our, we're approaching 50/50 in terms of the percentage of our ACV. Right now, slightly less than 50% of our ACV is on Pega Cloud, but Pega Cloud is growing faster, and so, you know, you know, in the near future, I, I would imagine based on just if you just look at the trend, that Pega Cloud will overtake client cloud. That's all happened, Blair, in the last five years. It's been a pretty fast shift. That's great, that's great. You know, I, I think Don, if you could just maybe describe a couple things for us here to help us understand. You have announced recently, you know, Pega GenAI as a product, or Pega Process AI. Can you just- Yeah ...just briefly describe what, what those are? Yeah. I'm gonna actually start with Pega Process AI. Pega Process AI is the analytical side of AI. If we think of AI as kind of being left brain, right brain, analytical AI, left-brained AI, makes decisions, right? Next-best-action decisions, process optimization decisions, et cetera. Most clients want that to be built on their own proprietary, private, first-party data. They don't want pre-built public models. They want that to run on their own data, because ultimately, they view those decisions as differentiating to their business. Differentiating in how they engage with their clients, differentiating in how they find optimizations, and how they run their workflows, et cetera. Process AI basically allows clients to take the data they already have in Pega from business processes, historical cases that they've run through, and find, based on that data, predictions for how future processes might behave. Imagine being able to predict at the outset of a payment dispute that regardless of the amount of effort you're gonna put into this, you're gonna write this off. Well, if I can predict that with 90% certainty the day you get it, and it's gonna cost you $50 to write it off, versus $100 to actually process it. Pursue- ... why not just write it off, right? Yeah. That's a net saving. If I can predict that there's an 80% likelihood that this is gonna miss a regulatory deadline, which results in you getting charged a fee, and I can predict that a week before that happens, let me automatically escalate that so you avoid actually having to pay that regulatory fine, or missing an SLA with a customer that's gonna degrade the customer relationship. That's the use case for Process AI, and that's an add-on to our existing case-based license for clients. Pega GenAI is a series of capabilities that are coming out, and I should say, by the way, Process AI is generally available, it's been generally available for over a year, so clients have been using it. Pega GenAI is a set of capabilities that are coming in Pega Infinity '23, which will be GA in September. It's about 20-plus generative AI powered, I would think of them as accelerators or boosters. Based on feedback with our clients as how they're thinking about using this generative AI stuff, one of the things that they're leaning into are human-in-the-loop use cases. I don't think many of our clients are ready to set generative AI loose to directly interact with our customers. Yeah ... or do things without a human in the loop. We've really focused on these human-in-the-loop use cases. For example, being able to build a business process or a workflow simply by typing the name of the workflow into Pega. I can just tell Pega to build me a student loan application workflow, and it'll come back and say: "Great, that should generally have these five high-level stages. Here are the five steps that would happen under each stage. This is what we think the data model should look like. Do you have any changes to that, or you want me to build an app that does that?" The great thing is, it accelerates the development process, because that might have been, like, two or three weeks of requirements gathering, and debating, and horse trading among team members, and now we take you from a blank page to something that you can really work with. It still gives the low-code developer or the workflow analyst, all the power they need to change it, override a suggestion, add their own step, remove steps, change the data model, add the data model, right? I'm accelerating the process. I'm also lowering the bar of entry, so that somebody You don't need a huge amount of Pega experience now to get started with building your workflow. You just need to type in the name of a workflow, and we'll start it for you. Other examples, you build a, you build a workflow, and you're gonna do this at an enterprise scale, you need to test it. Well, that means you need test data. Generative AI is really good at generating a bunch of test data, and that's a no-value add task that I now can take off the developer, so they can focus on adding business value to the application. Another use case for generative AI that we've got in Pega Infinity '23 is in this next-best-action space. One of the things that happens when customers shift their marketing from being kind of spray and pray, traditional, you know, campaign spam, to very one-to-one personal-... is the number of treatments that they actually need increases, because I'm no longer just giving everybody the same ad. I want to be able to say, like, "Yo, you're a millennial. I'm going to talk to you differently than I talk to a family that's just had their first child, or somebody who's about to retire." That means that a bottleneck often becomes the ability of marketing to generate all these treatments. Well, Gen AI is really good at saying, "We've got an offer for a new loan. Write me a treatment for a millennial audience. Write me a treatment for a, you know, couple with a new baby. Write me a treatment for somebody who's retiring." Then bring those back to the marketer so they can tweak them, override them, make changes to them. Again, I've accelerated the process of rolling out those new treatments, so I can have more personalization at scale faster than ever before. Then we've got also a series of use cases that fall more in the customer service space. A lot of, I don't think any organization is ready to put, for example, ChatGPT directly as a chatbot out talking to their customers, or any kind of generative AI. It's, it's not predictable enough. If you ask it the same question five times, you'll get seven different answers, right? There are really good natural language processing-based chatbots that organizations are very comfortable with converting with their customers. The challenge is you've got to train them. You've got to tell them, you know, how, that there are 100 different ways to say, "What's my balance?" in Dutch. Well, the neat thing is, I can ask generative AI to come up with a list of 100 different ways to say, "What's my balance?" in Dutch. Then I can feed those 100 different ways into my NLP chatbot. Now it knows that. I've accelerated how fast I can train these bots to make them more responsive. Those are the kinds of use cases where we're using generative AI to accelerate how you get automation and real-time decisioning in front of our clients' customers and in front of their employees faster. Yeah, you know what, Blair? Yeah, go ahead. Just to add to that, if you look at the way that the chatbots operate today, which could be mistaken for being AI-based, because they do seem to have a conversation with you. Yeah ... but really, it's very structured. It's like looking for certain responses, and if you say something, it'll say, "Did you mean to ask A, B, or C? Yeah. You click. It's really, it's really a decision tree model, right? Yeah. In terms of how it goes through that. That is helpful, but that isn't Gen AI, right? That's like. That's not generative AI. That's why I think clients are nervous about just saying, "I'm gonna put it out in the wild," and just let the actual... Right now, it's, it, it, it's kind of like almost, and it's really not AI, but it feels like AI. It's like the first step was, "I'm gonna build a decision tree," and it's basically gonna be like one of those novels where you, like, pick, and it says, "Go to page 88. Yeah. You know, like, like, you know, I think that when we get to the real-world use case of generative AI, like we know it to be, like, where it will actually sift through and give you a conversational answer, you really need to make sure the data lake that you're using, you got to do the testing. Yeah. I, I do think we're a little ways away from people taking that kind of risk on really deep relationship clients, because you don't know if you're going to offend them, be in the wrong language, make the wrong recommendation, tell them, "Oh, you'd be better off to buy the competitor's product." Like, I mean, you don't know where that's gonna go. I do think that that's the one I think people are really optimistic on, but I, I would say that that is where clients are a little nervous, right around on that. Yeah. One, one, one use case that we, we've also got in Infinity '23 that I do think is really interesting is more and more of our clients are seeing their customer service volume switch from voice to chat, because everybody wants to chat. It turns out, a lot of the customer service agents that got really good at voice, that doesn't naturally translate to being really good at interacting with chat. Yeah. They need to be trained, and they need to practice. Well, it turns out that ChatGPT does a really good job of simulating a customer in a chat. We're gonna have in customer service with Infinity '23, the ability to use generative AI to act like a customer with a specific personality. Maybe they're angry 'cause they've been waiting on the phone, or maybe they're really excited 'cause they really like this new product. You can have that customer with that personality simulate a chat interaction with your agents, so you can train your agents faster and get them more comfortable with handling the increasing chat volume that many of our clients are seeing. Yeah. That's a place where I feel like, again, it's helping the human better, and I think our, our clients are more comfortable with that kind of use case because it doesn't have the risk of, like, going directly to their customer and saying something they didn't predict was gonna happen. Yeah. Yeah. Have, have you had, what have you done so far in terms of beta testing of some of the, some of these new capabilities? So we've actually had out in the wild for a while, what we call the Self-study Buddy, which is basically, we took a lot of our knowledge content and our documentation, and without getting too technical, we used, there's a, there's an architecture for using Gen AI called embeddings. Basically what you do is you do prompt engineering. Yeah. You take an existing set of knowledge, and you use that knowledge to write the prompt to AI, to the Gen AI model, where you basically tell it, "I want you to answer this question, but I only want you to answer it based on this knowledge. Yeah, yeah. Right? "And if you can't find the answer in this, just say, I don't know." Right? So it's a really good way of using Gen AI, but restricting it to a known knowledge base. We've had that out in the wild with our own documentation and our community and support. We've seen really great responses from our client developers, from developers that are partners, because it allows them to quickly go and ask questions and get answers, and summarize what might be, like, 10 pages of documentation and turn it into, like, a five-step checklist. Yeah. Right? That's been a really kind of nice forward-looking indicator for us, that if you do this in a way where, again, you're empowering the human, and you're keeping them in the loop, you've got a degree of traceability. The other nice thing about this is, the buddy can actually give you its references. It can actually tell you, like, "By the way, I got this answer from this page... This source. Yeah. Yeah, you can check it, right? We found that when you have it with that kind of credibility and that kind of safeguard built in, it's been really embraced. We're excited about that. Is it... Sorry, Ken- Ken, you're on mute. You're on mute there. Sorry, I meant to click. Blair, I'll read you in, I'm looking at the sale. I'm looking at the Buddy one that Don's I'll read you, like, just a question that was asked this morning. "Write me a short summary of what Pega Customer Service can provide. Make sure that you differentiate it from our leading competitors, without naming these competitors explicitly in the note." Like, that's and it put, and it gave, you know, about a, about a 10-line response that you could use. Like, that's the kind of stuff... Like, if you think about the, where it's, it's not pulling from the public internet, it's pulling from our- Right ... content. It's, it's gonna, and it's, that's a powerful thing to use in a service channel, right? Yeah. You, you're helping to pre-train, before you release it to your customers, so that it's more effective, more effective for the, the guys that are actually gonna be building the processes, right? Yep. Exactly, exactly right. I mean, Ken, let me ask you this question, you know, and, and this technology is fascinating and, and powerful. What is Pega doing in terms of looking at using some of this stuff internally on your, turning it back on, onto Pega? What I just showed you is actually something we're using internally to train and enable our sales team. We're also- we gotta be careful that we don't push too many of these initiatives, right? Because then it becomes, like, a little bit of, you, you get, you know, you're, you're, you're spreading your value across it. We're focused on, enabling sales as an example, right? Things like, you know, writing script, inviting people to PegaWorld, things that, those were some of the test cases. We've also- we've, we're also, pushing this into our internal portal, which is one of the initiatives we have, which is people, employees, when they come to the portal, and they wanna... Normally, they would go to a dropdown, and they'd say, "Let me go into the People Hub, because I want to change my 401(k) election." There's a click, and then another click, and then a click to a third party. As opposed to saying, "Hey, I'd like to change my 401(k) election," and being able to interact, saying, "Oh, well, here's where you want to go." Naturally, the next step would be, "What would you like to do? We'll go ahead and execute that change for you." I think the first starting point is just serving up the information in a more kind of question-and-answer conversational environment. That's a, that's a very big kind of experience change for us internally. Then, I think the last, not the last, another frontier is really around how we... Don talked about quick starts, and how you use generative AI to get frameworks. You can actually use it not just to create an app, you can also use it to modify or improve an app, right? If you can so that, that's a. We use Pega for lots of applications. There's probably 50 use cases inside Pega, that we use Pega to actually execute those internal processes. We have teams that do application improvement around those, so naturally, that would speed up the throughput and the efficiency of that as well. Those are some examples where we're using it internally. That, that's great. That's great. Don, if I could take it back to you for a moment. You know, one of the things that you guys launched, about a year ago, was, was Pega Launchpad. Maybe you could talk about that a little bit, as to, as to, you know, where you're at with that, initiative, and, and what it could do, for your customers. Yeah. So the, the, the idea with Launchpad was, we, we had built a platform that was really, really good at workflow automation and AI-powered decisioning. It was really targeted at pretty big, substantial, often global organizations, that had internal workflows they needed to automate. We were seeing increasing demand from a lot of our partners, that they wanted to actually use a lot of the core capabilities in Pega's platform, around workflow automation, and being able to build user experiences across channels, et cetera, to deliver their own SaaS solutions to market. To come with, like, a pre-built workflow-based product that they could then go sell out to their customer base. There was, there was a need where some of the needs for that market are actually slightly different than some of the needs for the market that we have. Our Pega Cloud is largely single-tenant, and our clients like that, because it means we can, like, give them their own private data, and give them security, and VPN into some of their systems when necessary. Our partners who wanted to build solutions on Pega, they actually want multi-tenancy, 'cause they want to be able to operate at scale and have flexibility to scale this up and down with, with the kind of cost margins. Launchpad was designed to provide a platform specifically for SaaS providers, who want to then build and take their own workflow-based applications to market. We've been working over the past year with a number of early adopters, as they go through the initial development phase of those applications, so that they're getting close to the point to be able to then launch them out into public as sort of initial, to their initial early clients. ... what's the revenue model for Pega from, from Launchpad? The revenue model for Pega is essentially a revenue share model, right? Which is, which is, we will work with the intermediary, which is the ISV, as we might refer to them, where they will be the ultimate vendor to those clients. We will be the platform that they operate on, and we will help them think about the commercial models with those, with those use cases to optimize the model for them, and we would then, you know, get a share of that revenue based on that kind of flow-through. Okay, great. So it's GA now, is it, or is it still in beta? It is. We have a number of early adopters. It is, it is available, but not to anybody, just to the specific. You know, we've got essentially less than 10 that we're working with, that we believe are, you know, that are excited about it, that we think have the requisite Pega skills to be able to support it going after. I think 2023 and into 2024 is, I would, I would more say it's a, it, it's a selective release as opposed to the term GA. Okay. Eventually, we'll have the some of the AI capabilities on it and GenAI capabilities that we talked about earlier? It's Pega Cloud, so it would have accessibility to anything that Pega Cloud does, yes. Only limited by the imagination of the ISVs. Yes, that's right. That's great. That's great. Maybe, shifting gears a little bit again, you know, one of the things, Don, you and I talked about earlier this afternoon was, was this concept that was described as the age of the autonomous enterprise... Yeah ... which is, which is a, a longer term vision, but, you know, looks like the pieces are being put in place to make that happen. Maybe you could just describe that a little bit for us and, and to help people to understand what, what you mean by the autonomous enterprise. Yeah, I... I define the, let me define it, and then I'll talk a little bit about how we see this evolution happening. I define the autonomous enterprise as being able to use a combination of AI intelligence and automation to drive a business that is continuously self-optimizing. Imagine a business process that is continuously finding bottlenecks and breakpoints, and actually fixing itself, or at the very least, recommending out to the business, "Here's how and where you should invest in fixing and improving the process." Or, imagine a customer interaction that is learning in real time from previous customers, and actually getting smarter on what products it offers. And in fact, in some of this, we have clients who are doing this today. Many of our clients who use Pega for next best action, use a product called Pega Customer Decision Hub. They have models out there that are continuously learning and predicting, for example, what customers are likely to churn, and making a recommendation to save them, and learning from what works, and getting better at it over time, all with guidance from their data analytics teams, and marketing teams, and customer experience teams. So the autonomous enterprise is about carrying that vision forward. Over the past number of years, we've been putting together the kind of pieces of this. Like, we acquired Process Mining last year, because that's important, because that gets you the data about where bottlenecks are, not just in Pega processes, but in other processes that are running maybe outside of Pega. The work that we've done with Process AI to drive continuous optimization of the business process and predict where processes are going. What's really interesting with generative AI is it now begins to stitch all those pieces together. You know, I talked at PegaWorld in my keynote about this idea of an autopilot, right? Imagine a business leader being able to say, "I want you to optimize my claims process, and I want you to do it in such a way that reduces processing time, but maintains my customer Net Promoter Score." Now, I can go out and look, the autopilot can go and look and say, "Okay, I've looked at Process Mining data, I found a bottleneck. I think I can fix that bottleneck by inserting a process prediction that skips over it when it's not necessary. Do you want me to do that? I found a screen where you're constantly having to go back because the user is inputting incorrect data. I think you should go have a UI designer look at that screen and fix it, so you don't have to keep doing this rework. You want me to assign a task to a UX designer to go fix that screen? Great, I'll go do that." That's now a process that's continuously optimizing, and where I think of, like, where the next value, sort of, stage, escalation of value from generative AI comes, it's that. It's pushing businesses into this self-optimizing space, where not only are they getting automation value right now, but they're continuously finding ways to improve efficiency into their processes, or improve the efficacy of how they engage with their customers to do things like drive revenue or maintain their existing revenue streams. You know, I, I, it's interesting, I, I, spent a little time at your event, this summer and looking at the, the Process Mining, capabilities that, that, that you acquired, and maybe you could just describe what, what that is, and, and, 'cause it seems to be an area that, is, is a real value creator, I think, for, for customers. Yeah. So, we, we acquired a company that provides, you know, basically ability to look at historical process data. From that historical process data, find bottlenecks, so find places where the process is getting stuck or taking longer than it should. Find places where rework is happening. Find places where the process is, like, diverging from the ideal path or the happy path. One of the reasons we really like the technology and the company that we acquired was, they already had built in some not just the ability to diagram and draw the process, but be able to do, like, root cause analysis and tell you, like, "This is broken, and here's how to fix it." So we spent since the acquisition, we've been making sure that's really tightly integrated into the Pega platform, so that if you've got an existing process running in Pega, all you need to do is turn Process Mining on, and we'll start finding new opportunities for you to make that process even better. It additionally can import processes from other systems, like SAP, so if you want to take in your SAP data, it can also do that. The important thing here is, and I think this is distinct from how other process mining has come at it, we come at it from the endpoint of ultimately, we want to execute this business process, so we can automate it for you. We don't just want to mine it and find, like, opportunities. We're fundamentally an execution and an automation engine. Process mining is now just an input into that engine, so that it can find continuous optimizations and ways to get better. I think the intersection of now having the process mining data, plus our core capability in being able to drive automation and AI decisioning into the process, that just is a huge value opener for our clients. You, you've, you've got a lot of innovation happening this year, this past 12 months at, at, at the business. How are you getting that out to customers? How, how are you, you know, educating them and, and getting them to really, start to pick up and utilize the power that, that you're, you're putting into the platform? Yeah. You know, one of the things I think, you know, Peter's talked about is really making sure, one, that we've got focus on the having very deep and intimate relationships with a select set of customers. One of the things that we're really focused on is the large-scale organizations that really have the opportunity to get great value from this, where we're already a proven, trusted partner. That gives us the opportunity to make sure that we are in the door, walking the halls, you know, advising the, the centers of excellence and lead teams, so that they're continuously driving and taking advantage of this. You know, we're also continuously making sure, like, you know, one of the reasons why we're looking at things like generative AI, plugging into our documentation, and plugging into our enablement and training, so that we continuously provide ways for our customers to self-serve and self-enable on this stuff. It's very easy for them to go and find out what are the latest and greatest capabilities? How could I use this with my business process? More and more, you know, I think as Alan kind of hinted at his PegaWorld keynote, he talked about having Pega expertise at your fingertips. I think the neat thing is, by integrating some of this stuff that we've done now with generative AI and our content and knowledge base directly into our development environment, we can actually take it exactly to where our customers are, so they don't even need to go looking for it. We're just kind of prompting them right in the design experience. Like, "Hey, by the way, here's a new capability," or, "I think you could use this, you could do that." I think that, combined with the very deep and kind of personal relationships we build at a client-by-client level, really allow us to help ensure both that our clients are getting value, and frankly, we're getting the kind of feedback we need from them to continuously improve and make sure that what we're delivering is the right thing for their needs. It, it seems like, these, adding this, this additional intelligence on top of your platform or into your platform in the various areas should, should really make the product stickier, as well over time, should it not? Yeah, I mean, I think I'll let Ken and Peter speak to our retention rates. The thing that I would say is we tend to do pretty mission-critical processes for our clients, and those mission-critical processes tend to be heavily integrated into their existing systems, into their existing channels and front ends. The combination of that, plus the fact that we've now got this data, that we can help our clients mine for increased optimization and increased efficiency, I think helps ensure that we are pretty sticky with our clients. I, I think, I think that we, you know, we have historically had very high retention rates, Blair. I think the difference with what this does is, I think this... We become a, you know, potentially a very critical partner to our clients' initiatives around how they drive efficiency into their organization to leverage AI. Resources are very scarce, both in dollars and in people, and it's very hard for companies to, in today's world, to just say, "Well, I'll just hire another 300 CSRs. I'll just hire another 500- Yeah ... assessors." Like, those, those days don't really exist. I mean, those resources are scarce, and, you know, just with the way the world has changed, they're not even co-located in many cases, in the same location. You don't even get the benefit of having everybody in the same building. People are working remote. They're all over time zones, kind of the fall. You really are very dependent on the next person you need to hire, the next day. That's a very fragile business model when you're trying to support clients in digital channels, like Don mentioned chat earlier. I think that's the pivot that our clients are making, is really making this autonomous kind of enterprise, the kind of analogy is be real, right? To where they can actually say, "You can talk to us anytime you want, from anywhere, around anything, and you're not gonna have to wait to get in the queue. Yeah ... someone on the call with you. I think that's especially true for some of, like, the very specialized roles that clients need. Like, they're, they are never gonna be able to hire enough data scientists to- Yeah ... to, like, manually build the AI models they need on their own. The fact that we can come up with, like, Pega Customer Decision Hub and Pega Process AI, with an engine that can actually look at the existing first-party data they have and automatically create a self-learning model that can then optimize the customer engagement or find efficiencies and predictions in the business process, means that even without having a team of dedicated data scientists looking at this problem, we're helping them surface efficiencies and get benefits from the AI technology. We're doing it in a way that's proven to be transparent, and explainable, and protects their data and, it, you know, tests for ethical bias, and all the stuff that clients know they need to do. We've got a decade of experience doing that really well. I think especially when clients look at, like, how do I turn all this AI from a science project into value? Yeah. We've got a history of doing that, that doesn't require them to find 100 PhDs to make it happen. Yeah, which are, which are expensive and scarce. Yeah. All right. Well, listen, I think we're up against our time here, but this has been a fascinating discussion, Don. Ken and Peter, thank you, thank you very much for taking the time out to give us a little more insight into what Pega is doing with this technology, and looking forward to the full release here of the next Pega Infinity. Excellent. Thank you. Thanks, Blair.